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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2310.08383 |
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| _version_ | 1866911856055025664 |
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| author | Hira, Kausik Zaki, Mohd Sheth, Dhruvil Mausam Krishnan, N M Anoop |
| author_facet | Hira, Kausik Zaki, Mohd Sheth, Dhruvil Mausam Krishnan, N M Anoop |
| contents | The discovery of new materials has a documented history of propelling human progress for centuries and more. The behaviour of a material is a function of its composition, structure, and properties, which further depend on its processing and testing conditions. Recent developments in deep learning and natural language processing have enabled information extraction at scale from published literature such as peer-reviewed publications, books, and patents. However, this information is spread in multiple formats, such as tables, text, and images, and with little or no uniformity in reporting style giving rise to several machine learning challenges. Here, we discuss, quantify, and document these challenges in automated information extraction (IE) from materials science literature towards the creation of a large materials science knowledge base. Specifically, we focus on IE from text and tables and outline several challenges with examples. We hope the present work inspires researchers to address the challenges in a coherent fashion, providing a fillip to IE towards developing a materials knowledge base. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_08383 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Reconstructing Materials Tetrahedron: Challenges in Materials Information Extraction Hira, Kausik Zaki, Mohd Sheth, Dhruvil Mausam Krishnan, N M Anoop Computation and Language Materials Science The discovery of new materials has a documented history of propelling human progress for centuries and more. The behaviour of a material is a function of its composition, structure, and properties, which further depend on its processing and testing conditions. Recent developments in deep learning and natural language processing have enabled information extraction at scale from published literature such as peer-reviewed publications, books, and patents. However, this information is spread in multiple formats, such as tables, text, and images, and with little or no uniformity in reporting style giving rise to several machine learning challenges. Here, we discuss, quantify, and document these challenges in automated information extraction (IE) from materials science literature towards the creation of a large materials science knowledge base. Specifically, we focus on IE from text and tables and outline several challenges with examples. We hope the present work inspires researchers to address the challenges in a coherent fashion, providing a fillip to IE towards developing a materials knowledge base. |
| title | Reconstructing Materials Tetrahedron: Challenges in Materials Information Extraction |
| topic | Computation and Language Materials Science |
| url | https://arxiv.org/abs/2310.08383 |